Search Results for author: Frédo Durand

Found 11 papers, 9 papers with code

Plug-and-Play Algorithms for Video Snapshot Compressive Imaging

no code implementations13 Jan 2021 Xin Yuan, Yang Liu, Jinli Suo, Frédo Durand, Qionghai Dai

On the other hand, applying SCI to large-scale problems (HD or UHD videos) in our daily life is still challenging and one of the bottlenecks lies in the reconstruction algorithm.

Demosaicking Denoising

AsyncTaichi: On-the-fly Inter-kernel Optimizations for Imperative and Spatially Sparse Programming

no code implementations15 Dec 2020 Yuanming Hu, Mingkuan Xu, Ye Kuang, Frédo Durand

These domain-specific optimizations further make way for classical general-purpose optimizations that are originally challenging to directly apply to computations with sparse data structures.

When and how CNNs generalize to out-of-distribution category-viewpoint combinations

1 code implementation15 Jul 2020 Spandan Madan, Timothy Henry, Jamell Dozier, Helen Ho, Nishchal Bhandari, Tomotake Sasaki, Frédo Durand, Hanspeter Pfister, Xavier Boix

In this paper, we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and 3D viewpoint on OOD combinations, and identifying the neural mechanisms that facilitate such OOD generalization.

Object Recognition Viewpoint Estimation

Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings

1 code implementation CVPR 2020 Amy Zhao, Guha Balakrishnan, Kathleen M. Lewis, Frédo Durand, John V. Guttag, Adrian V. Dalca

We present a probabilistic model that, given a single image of a completed painting, recurrently synthesizes steps of the painting process.

DiffTaichi: Differentiable Programming for Physical Simulation

2 code implementations ICLR 2020 Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, Frédo Durand

We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.

Physical Simulations

Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation

1 code implementation18 Apr 2019 Ronnachai Jaroensri, Camille Biscarrat, Miika Aittala, Frédo Durand

Unfortunately, the commonly used additive white noise (AWGN) models do not accurately reproduce the noise and the degradation encountered on these inputs.

Demosaicking Denoising +1

Differentiable Monte Carlo Ray Tracing through Edge Sampling

1 code implementation SIGGRAPH 2018 Tzu-Mao Li, Miika Aittala, Frédo Durand, Jaakko Lehtinen

We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters.

Learning-based Video Motion Magnification

2 code implementations ECCV 2018 Tae-Hyun Oh, Ronnachai Jaroensri, Changil Kim, Mohamed Elgharib, Frédo Durand, William T. Freeman, Wojciech Matusik

We show that the learned filters achieve high-quality results on real videos, with less ringing artifacts and better noise characteristics than previous methods.

Deep Bilateral Learning for Real-Time Image Enhancement

2 code implementations10 Jul 2017 Michaël Gharbi, Jiawen Chen, Jonathan T. Barron, Samuel W. Hasinoff, Frédo Durand

For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms.

Image Enhancement

What do different evaluation metrics tell us about saliency models?

1 code implementation12 Apr 2016 Zoya Bylinskii, Tilke Judd, Aude Oliva, Antonio Torralba, Frédo Durand

How best to evaluate a saliency model's ability to predict where humans look in images is an open research question.

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